Soft Computing Based Range Facial Recognition Using Eigenface

  • Yeung-Hak Lee
  • Chang-Wook Han
  • Tae-Sun Kim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3994)


The depth information in the face represents personal features in detail. In particular, the surface curvatures extracted from the face contain the most important personal facial information. These surface curvature and eigenface, which reduce the data dimensions with less degradation of original information, are collaborated into the proposed 3D face recognition algorithm. The principal components represent the local facial characteristics without loss for the information. Recognition for the eigenface referred from the maximum and minimum curvatures is performed. The normalized facial images are also considered to enhance the recognition rate. To classify the faces, the cascade architectures of fuzzy neural networks, which can guarantee a high recognition rate as well as parsimonious knowledge base, are considered. Experimental results on a 46 person data set of 3D images demonstrate the effectiveness of the proposed method.


Face Recognition Recognition Rate Face Image Memetic Algorithm Fuzzy Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yeung-Hak Lee
    • 1
  • Chang-Wook Han
    • 1
  • Tae-Sun Kim
    • 2
  1. 1.School of Electrical Engineering and Computer ScienceYeungnam UniversityGyongbukSouth Korea
  2. 2.Department of Digital Electronic EngineeringKyungwoon UniversityKyungbukSouth Korea

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